Identification of multiple mode models via Distributed Particle Swarm Optimization

Abstract

AbstractThis paper considers the identification of multiple-mode systems, and introduces a new method to estimate the subsystem parameters of piece-wise affine systems. First, the notion of multiple-mode linear regression model and the way to reduce its identification problem to an optimization one are introduced. Second, since the introduced optimization problem is inherently ill-conditioned and nonconvex, a new technique named distributed PSO (particle swarm optimization) is developed to avoid being trapped in suboptimal solutions. The proposed identification scheme can handle the identification of piece-wise affine systems without any prior knowledge about their mode transitions and has no difficulty to handle a large number of data samples, which is an distinguished feature of the proposed method. Finally, an experiment with a set of I/O data from a DC motor system is given to demonstrate the effectiveness of the proposed identification method and to evaluate the performance of the proposed optimization technique.